powershap VS NVTabular

Compare powershap vs NVTabular and see what are their differences.

powershap

A power-full Shapley feature selection method. (by predict-idlab)

NVTabular

NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems. (by NVIDIA-Merlin)
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powershap NVTabular
1 1
180 1,008
3.9% 0.5%
3.5 5.5
7 days ago 8 days ago
Python Python
GNU General Public License v3.0 or later Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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powershap

Posts with mentions or reviews of powershap. We have used some of these posts to build our list of alternatives and similar projects.
  • [R] PowerShap: A power-full Shapley feature selection method.
    1 project | /r/MachineLearning | 20 Jun 2022
    We are glad to present our novel shap-based wrapper feature selection method called PowerShap! This method uses statistical hypothesis testing and power calculations on Shapley values, enabling fast and intuitive wrapper-based feature selection. The complete library and methods are fully compatible with Sklearn, LightGBM, CatBoost, and more are coming in further following releases and the library can be found here: https://github.com/predict-idlab/powershap! The library is open-source and usable out-of-the-box as shown in the video!

NVTabular

Posts with mentions or reviews of NVTabular. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-10-08.

What are some alternatives?

When comparing powershap and NVTabular you can also consider the following projects:

cascade - Lightweight and modular MLOps library targeted at small teams or individuals

dbt-expectations - Port(ish) of Great Expectations to dbt test macros

evalml - EvalML is an AutoML library written in python.

Scio - A Scala API for Apache Beam and Google Cloud Dataflow.